The document outlines a predictive model developed using a historical credit dataset to improve loan approval decisions, noting that approximately 11% of loans currently granted default. It details the methodology, including data quality checks, variable selection using different methods, and the final model's performance metrics, revealing that a significant percentage of defaulters can be classified. Additionally, it provides characteristics to avoid targeting in loan approvals, based on the model's estimates.

1. German
Bank
Loan
Approval
Decision
Model
Mohamed
Ibrahim
Rahul
Goel
Akanksha
Jain
Chhavi
Sharma

2. "Houston,
We've
Had
a
Problem"
On
average,
one
of
every
nine
loans
you
grant
is
defaulted
on
2

3. Objec<ve
&
Background
Objec<ve:
Use
historical
credit
dataset
to
develop
a
predicAve
model
to
make
beBer
loan
approval
decisions
in
the
future
Background:
On
an
average,
~11%
loans
granted
are
defaulted
in
the
current
scenario.
Scope:
German
Credit
Dataset
– 1000
entries
&
20
variables
– Historical
pre-­‐loan
data
– Dependent
variable:
Good_Bad
(Binary)
Steps:
– Data
Quality
Check
– Variable
exploraAon
and
transformaAon
– Method
SelecAon(forward,
backward,
stepwise)
– Removal
of
outliers
– Run
the
LogisAc
Regression
Model
3

4. Variable
Transforma<on
4

5. Variable
Transforma<on(cont..)
5

6. Data
Variables
Data
Drill
Down
6

7. Variable
Selec<on
1
2
3
4
Full
Model
using
STEPWISE
variable
selec<on
method
Full
Model
using
BACKWARD
variable
selec<on
method
Full
model
using
FORWARD
variable
selec<on
method:
Model
all
variables
from
STEPWISE
+
variable
age_new
Variables:
checking_new
duraAon_new
history_new
purpose_new
savings_new
installp
marital
coapp
other
foreign_new
Variables:
checking_new
duraAon_new
history_new
purpose_new
savings_new
installp
marital
coapp
other
foreign_new
Variables:
checking_new
duraAon_new
history_new
purpose_new
savings_new
installp
marital
coapp
other
foreign_new
amount_new
age_new
Percent
Concordant
82.3
c
0.823
Hosmer
–
Lemeshow
Test
:
0.9398
SL
Entry:
0.1
Variables:
checking_new
duraAon_new
history_new
purpose_new
savings_new
installp
marital
coapp
age_new
other
foreign_new
age_new
Percent
Concordant
82.0
c
0.820
Hosmer
–
Lemeshow
Test
:
0.7475
Variables:
checking_new
duraAon_new
history_new
purpose_new
savings_new
installp
marital
coapp
age_new
other
foreign_new
age_new
Percent
Concordant
84.1
c
0.841
Hosmer
–
Lemeshow
Test
:
0.6367
SL
Entry:
0.1
SL
Stay:
0.05
SL
Entry:
0.1
SL
Stay:
0.05
Percent
Concordant
81.8
c
0.818
Hosmer
–
Lemeshow
Test
:
0.7535
SL
Entry:
0.1
SL
Stay:
0.05
Percent
Concordant
81.8
c
0.818
Hosmer
–
Lemeshow
Test
:
0.7535
SL
Stay:
0.05
5
Stepwise
with
age_new
aLer
removing
outliers
7

8. Final
Model
8

9. 42.3%
defaulters
were
classified
as
“bad”
at
55%
or
more
9

10. Parameter
Es<mates
10

11. Parameter
Es<mates
(cont..)
11

12. Who
“not”
to
target
The
odds
raAo
&
parameter
esAmates
suggest
that
the
business
should
avoid
targeAng
individuals
with
the
following
characterisAcs
(not
arranged
in
order
of
priority)
–
–
–
–
–
–
–
Have
lower
balances
in
checking
&
savings
account
Have
a
delinquent
credit
history
Do
not
have
a
guarantor
Have
other
installment
plans
A
non
resident
Lower
age
High
debt-­‐to-­‐income
raAo
**
The
list
of
characterisAcs
suggested
by
the
model
is
for
a
given
set
of
1000
data
points
and
should
not
be
extrapolated
to
other
scenarios.
This
interpretaAon
is
only
for
the
purpose
of
a
classroom
project
and
should
not
be
used
otherwise.
12

13. Thank
You
13

14. Appendix
•
•
•
•
CorrelaAon
Results
Proc
Means
&
VIF
Test
Outliers
Proc
Rank
14

15. Correla<on
Results
15

16. Means
&
VIF
Variance
Infla<on
Factor
Proc
Means
16

17. List
of
16
Outliers
in
the
Dataset
17

18. Rank
Results
18